1. Technical Field of the Present Disclosure
The present disclosure relates generally to the field of seizure identification and more particularly to the field of identifying seizures by monitoring and comparing heart data from two or more windows.
2. Background of the Present Disclosure
Seizures generally are characterized by abnormal/excessive neural activity in the brain. Seizures may involve loss of consciousness or awareness, and cause falls, uncontrollable convulsions, etc. Significant injuries may result not only from the neuronal activity in the brain but also from the associated loss of motor function from falls or the inability to perceive and/or respond appropriately to potential danger or harm.
It is important to identify seizures as quickly as possible after the onset of the seizure to allow corrective action to be taken immediately, including administering therapy or intervening to prevent injury to the patient. It is also important to be able to identify and record seizures that have occurred to accurately assess the state of the patient's condition and determine whether therapies are effective or should be modified. Seizure detection algorithms have been proposed using a variety of body parameters to detect seizures, including brain waves (e.g., electroencephalogram or EEG signals), heart beats (e.g., electrocardiogram or EKG signals), and movements (e.g., triaxial accelerometer signals). See, e.g., U.S. Pat. No. 5,928,272, and U.S. application Ser. No. 12/770,562, both of which are hereby incorporated by reference herein.
Detection of seizures using heart data requires that the algorithm distinguish between pathological changes in the detected heart signal (which indicate a seizure) and non-pathological changes that may be similar to pathological changes but involve normal physiological functioning. For example, the heart rate may rise both when a seizure event occurs and when the patient exercises, climbs stairs or performs other physiologically demanding acts.
Seizure detection algorithms, in some instances, may need to distinguish between changes in heart rate due to a seizure and those due to exertional or positional/postural changes. As noted, it is important to detect seizures quickly and accurately. However, current algorithms fail to provide rapid and accurate detection. Current algorithms also fail to provide an indication of when the seizure has ended and the danger to the patient is reduced. The present invention addresses limitations associated with existing cardiac-based seizure detection algorithms.